NDLI: Multimodal learning for autonomous underwater vehicles from visual and bathymetric data

Content Provider	IEEE Xplore Digital Library
Author	Rao, D. De Deuge, M. Nourani-Vatani, N. Douillard, B. Williams, S.B. Pizarro, O.
Copyright Year	2014
Description	Author affiliation: Australian Centre for Field Robot., Univ. of Sydney, Sydney, NSW, Australia (Rao, D.; De Deuge, M.; Nourani-Vatani, N.; Williams, S.B.; Pizarro, O.) \|\| NASA Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA (Douillard, B.)
Abstract	Autonomous Underwater Vehicles (AUVs) gather large volumes of visual imagery, which can help monitor marine ecosystems and plan future surveys. One key task in marine ecology is benthic habitat mapping, the classification of large regions of the ocean floor into broad habitat categories. Since visual data only covers a small fraction of the ocean floor, traditional habitat mapping is performed using shipborne acoustic multi-beam data, with visual data as ground truth. However, given the high resolution and rich textural cues in visual data, an ideal approach should explicitly utilise visual features in the classification process. To this end, we propose a multimodal model which utilises visual data and shipborne multi-beam bathymetry to perform both classification and sampling tasks. Our algorithm learns the relationship between both modalities, but is also effective when visual data is missing. Our results suggest that by performing multimodal learning, classification performance is improved in scenarios where visual data is unavailable, such as the habitat mapping scenario. We also demonstrate empirically that the model is able to perform generative tasks, producing plausible samples from the underlying data-generating distribution.
Starting Page	3819
Ending Page	3825
File Size	2930721
Page Count	7
File Format	PDF
ISBN	9781479936854
DOI	10.1109/ICRA.2014.6907413
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Training Encoding Correlation Vectors Image reconstruction Oceans
Content Type	Text
Resource Type	Article

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